scivae | VAE package for reproducible generative models

scivae is a Jupyter Notebook library. scivae has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Check out our docs: If you use this please cite: scivae is a wrapper around the keras AE that allows you to build/save/visualise with a variational autoencoder. Blogs & notebooks used as references are noted in the code and a couple at the end of this README. The primary difference between a VAE and a normal AE is in how the loss function is computed. Here the loss has been abstracted out to the loss class (in loss.py) where we can use a number of loss metrics MMD, KL and combine this with MSE or COR loss. The VAE (in vae.py) class has the general VAE structure. Saving has been implemented of the VAE state so that you can re-use your trained model on the same data and get the same latent space (or use the trained VAE on new data). Optimiser was a temporary deviation where we can pass in a VAE structure and using an evolutionary algorithm the optimisation class will try to get the best VAE structure. This will be returned. Validate allows for running simple validations using scikitlearn i.e. if your primary interest is to get a meaningful latent space that captures the key features of the dataset, it can be good to compare how much "information" has been captured between your classes. A good way of measuring this is by passing through the latent space and a set of labels and seeing if a simple classifier can distingush your classes better than with the raw data.